In a data-driven, global, always-on world, databases are the engines that let businesses innovate and transform. As databases get more sophisticated and more organizations look for managed database services to handle infrastructure needs, there are a few key trends we’re seeing. Here’s what to watch.

With unemployment application submissions reaching record numbers over the past year, state and local agencies in the United States have faced the challenge of processing unprecedented numbers of claims per week. The digital infrastructure most agencies have in place is unable to handle this volume, resulting in constituents waiting longer, and bad actors taking advantage of vulnerable systems. The Department of Labor Inspector General estimates that $63 billion in claims distributed is either an improper payment or fraud. 

Validating claims also requires secure data sharing with other agencies for document and identity verification. Government leaders need a way to allow case adjudicators to quickly and confidently release backlogged claims, integrate with existing systems, and segment legitimate claims from potentially fraudulent ones — all within limited government budgets — securely and at scale.

Implementing a fraud detection solution on Google Cloud

States were under pressure to release payments, while also filtering out potentially fraudulent claims. SpringML and Google Cloud developed a framework to give adjudicators a reliable verification process that quickly filters potentially fraudulent claims, while processing the remaining claims so benefits reach citizens in a timely manner. SpringML and Google Cloud, applied AI-powered machine learning models to detect anomalous patterns in large datasets. Using Google Cloud tools, SpringML implemented a solution to streamline workflows, improve efficiencies, automate processes and identify potentially fraudulent claims.

SpringML used a variety of Google Cloud products to deliver a fraud detection solution, including:

• Google Cloud Storage to store and manage data
• BigQuery to store tabular data and BigQuery Machine Learning (BQML) to conduct machine learning on that data
• AutoML solutions to build predictive models and risk scoring
• Visualization tools such as Looker and Data Studio to present data and help government leaders make informed decisions.

Implementing machine learning to detect improper payments allows agencies to classify claims as “fraud” or “not fraud” based on the number of flags, as well as prioritize the most urgent claims. Deploying intelligent virtual agents to handle frequently asked questions meant that live agents could focus their time on more challenging cases. 

Even once the pandemic is behind us, there will be bad actors trying to take advantage of overwhelmed or legacy systems. We’ve identified a few best practices for agencies managing enormous case loads and looking to improve improper payment analytics: 

• Move your systems to the cloud. Many on-premises legacy systems can’t update their applications and scale to meet the volume of claims. Moving to a cloud environment enables rapid solution deployment and ingestion of large amounts of data without fear of overloading the system. The cloud scales with you–cost-effectively and securely. 
• Understand patterns in the data. The answer is always in the data — we used deep analysis to help uncover suspicious patterns in large data sets. We implemented unsupervised machine learning to learn behaviors and create configurable rules that adjust to new information that comes into the system. We can uncover patterns that are likely associated with fraud – ones that a human might have missed. 
• Use AI/ML tools to automate your existing systems and teams. These tools enable humans to work smarter and more efficiently. We automate anomaly detection and create dashboards for adjudicators to rapidly process claims. We are enabling the Wisconsin Department of Workforce Development by implementing automatic calculations and processing of recharge amounts, resulting in faster processing times and fewer human errors. Proactive fraud detection and timely calculation of recharge payment allowed DWD to ensure the benefits reached the right individuals.
• Build flexibility into your systems. We discovered that fraud patterns change over time. For instance,flags for fraud during March-May 2020 were vastly different from those we found in June-July 2020. Google Cloud tools make it easy to continually update algorithms to detect patterns and integrate external data sources.

Using Google Cloud tools, we can update digital infrastructure and incorporate machine learning best practices to help organizations efficiently process large volumes of claims and identify high probability fraudulent ones. SpringML provides consulting and implementation services and industry-specific analytics solutions that deliver high-impact business value to accelerate data-driven digital transformation. Learn more about fraud detection and how to improve improper payments analytics by watching our webinar. 

In our conversations with technology leaders about data-driven transformation using Google Data Cloud –  industry’s leading unified data and AI solution – , one important topic is incorporating continuous intelligence to move from answering questions such as “What has happened? to questions like “What is happening?” and “What might happen?”. The core to this evolution is the need for an underlying data processing that not only provides powerful real-time capabilities for events happening close to origination, but also brings together existing data sources under one unified data platform to enable organizations to draw insights and take actions holistically. Dataflow, Google’s cloud-native data processing and streaming analytics platform, is a key component of any modern data and AI architecture and data transformation journey, along with BigQuery, Google’s internet-scale warehouse with built-in streaming, BI engine and ML; Pub/Sub, a global no-ops event delivery service; and Looker, a modern BI and embedded analytics platform. One of the key evaluation factors is potential economic value of Dataflow to their organization, particularly in the context of engaging other stakeholders is key for many of the leaders that we engage with. So we commissioned Forrester Consulting to conduct a comprehensive study on the impact that Dataflow had on their organization by interviewing actual customers . 

Today we’re excited to share our commissioned study conducted by Forrester Consulting, the Total Economic Impact™ of Google Cloud Dataflow, which allows data leaders to understand and quantify the benefits of Dataflow, and use cases it enables. Forrester conducted interviews with Dataflow customers to evaluate the benefits, costs, and risks of investing in Dataflow across an organization. Based on their interviews, Forrester identified major financial benefits across four different areas: business growth, infrastructure cost savings, data engineer productivity, and administration efficiency. In fact, Forrester found that customers adopting Dataflow can achieve a 55% boost in developer productivity and a 50% reduction in infrastructure costs. In fact, Forrester projects that customers adopting Dataflow can achieve a range of up to 171% Return on Investment (ROI) and a less than six months payback period. Customers can now use figures in the report to compute their own Return on Investment (ROI) and payback period.

“Dataflow is integral to accelerating time-to-market, decreasing time-to-production, reducing time to figure out how to use data for use cases, focusing time on value-add tasks, streamlining ingestion, and reducing total cost of ownership.” – Lead technical architect, CPG

Let’s take a deeper look at the ways that Forrester found that Dataflow can help you achieve your goals and unlock your business potential. 

Benefit #1: Increase data engineer productivity by 55%

Developers can choose among a variety of programming languages to define and execute data workflows. Dataflow also seamlessly integrates with other Google Cloud Platform and open source technologies to maximize value and applicability to a wide variety of use cases. Dataflow streamlined workflows with code reusability,dynamic templates, and the simplicity of a managed service. Engineers trusted pipelines to run correctly and adhere to governance. Data engineers avoided laborious issue-monitoring and remediation tasks that were common in the legacy environments such as poor performance, lack of availability, and failed jobs. Teams valued the language flexibility and open source base.

“Dataflow provided us with ETL replacement that opened limitless potential use cases and enabled us to do smarter data enhancement while data remains in motion.” — Director of data projects, financial services

Benefit #2: Reduce infrastructure costs by up-to 50% for batch and streaming workloads 

Dataflow’s serverless autoscaling and discrete control of job needs, scheduling, and regions eliminated overhead and optimized technology spending. Consolidating global data processing solutions to Dataflow further eliminated excess costs while ensuring performance, resilience, and governance across environments. Dataflow’s unified streaming and batch data platform gives organizations the flexibility to define either workload in the same programming model, run it on the same infrastructure, and manage it from a single operational management tool. 

“Our costs with our cloud data platform using Dataflow are just a fraction of the costs we faced before. Now we only pay for cloud infrastructure consumption because the open source base helps us avoid licensing costs. We spend about $120,000 per year with Dataflow, but we’d be spending millions with our old technologies.” – Lead technical architect, CPG

Benefit #3: Increase top-line revenue by improving customer experience and retention with payback time of < 6 months

Streaming analytics is an essential capability in today’s digital world to gain real-time actionable insights. Likewise, organizations must also have flexible, high- performance batch environments to analyze historical data for building machine learning models, business intelligence, and advanced analytics. Dataflow enabled real-time streaming use cases, improved data enrichment, encouraged data exploration,improved performance and resiliency, reduced errors, increased trust, and eliminated barriers to scale. As a result, organizations provided customers with more accurate, relevant, and in-the-moment data-backed services and insights — boosting customer experience, creating new revenue streams, and improving acquisition, retention, and enrichment.

“It’s already been proven that we are getting more business [with Dataflow] because we can turn around results faster for customers.” – VP of technology, financial services technology

“When we provide data to our customers and partners with Dataflow, we are much more confident in those numbers and can provide accurate data within a minute. Our customers and partners have taken note and commented on this. It’s reduced complaints and prevented churn.” – Senior software engineer, media

Other benefits 

Eliminated administrative overhead and toil

As a cloud-native managed service, all administration tasks such as provisioning, scaling, and updates are automatically handled by Google Cloud. Teams no longer need to manage servers and related software for legacy data processing solutions. Admins also streamlined processes for setting up data sources, adding pipelines, and enforcing governance.

Saved business operations costs for support teams and data end users

Dataflow improved the speed, quality, reliability, and ease of access to data for insights for general business users, saving time and empowering users to drive better data-backed outcomes. It also reduced support inquiry volume while automating manual job creation.

What’s next?

Download the Forrester Total Economic Impact study today to dive deep into the economic impact Dataflow can deliver your organization. We would love to partner with you to explore the potential Dataflow can unlock in your teams. Please reach out to our sales team to start a conversation about your data transformation with Google Cloud.

For years, retailers have been told that they must embrace a litany of new technologies, trends, and imperatives like online shopping, mobile apps, omnichannel, and digital transformation. In search of growth and stability, retailers adopted many of these, only to realize that for every box they ticked, there was another one waiting.

And then the pandemic hit, along with rising social movements and increasingly harsh weather. Some retailers were more prepared to take on these disruptions than others, which crystallized a new universal truth across the industry: the ability to adapt on the fly became the most important trait to survive and thrive.

Today’s retail landscape has surfaced both existing and new challenges for specialty and department store retailers. Approximately 88% of purchases previously occurred within a store environment. Now, it’s closer to 59%, with the remainder done online or through other omni methods. 

With such constant change and upheaval, it can feel like the mantra now is ABP: always be pivoting. 

The big question isn’t just how to maintain constant momentum and agility—it’s how to do it without sapping your workforce, your inventory, or your profits in the process. The pivot is now a given. What matters is how you do it.

Adapting requires a flexible base of technology that allows retailers to shift and scale seamlessly with the needs of the moment. 

They need to be able to leverage real-time insights and enhance customer experiences rapidly, online and in the real world (not to mention the growing hybridization that’s AR and VR). They need to modernize their stores to power engaging consumer and associate experiences. They need to enhance operations for rapid scaling between full operations and digital-only offerings.

To help retailers achieve these goals and more, Google Cloud is honing a trio of essential innovations: demand forecasting that harnesses the power of data analytics and artificial intelligence; enhanced product discovery to improve conversion across channels; and the tools to help create the modern store experience.

In other words, here’s some of the biggest ways we’re ready to help you pivot.

Pivot point 1: Harnessing data and AI for demand forecasting with Vertex AI

One of the greatest challenges for retailers when building organizational flexibility is managing inventory and the supply chain. 

We are in the midst of one of the worst global supply chain crises, stemming from soaring demand and logistics issues brought on by the pandemic. This crisis has only heightened the challenge retailers face when assessing demand and product availability. Even in normal times, mismanagement of inventory can add up to a trillion-dollar problem, according to IHL Group (costing $634 billion in lost sales worldwide each year, while overstocks result in $472 billion in lost revenues due to markdowns). 

On the flipside, optimizing your supply chain can lead to greater profits. For instance, McKinsey predicts that a 10% to 20% improvement in retail supply chain forecasting accuracy is likely to produce a 5% reduction in inventory costs and a 2% to 3% increase in revenues.

Some of the challenges related to demand forecasting include:

• Low accuracy leads to excess inventory, missed sales, and pressure on fragile supply chains.
• Real drivers of product demand are not included, because large datasets are hard to model using traditional methods.
• Poor accuracy for new product launches and products that have sparse or intermittent demand.
• Complex models are hard to understand, leading to poor product allocation and low return on investment on promotions.
• Different departments use different methods, leading to miscommunication and costly reconciliation errors.

AI-based demand forecasting techniques can help. Vertex AI Forecast supports retailers in maintaining greater inventory flexibility by infusing machine learning into their existing systems. Machine learning and AI-based forecasting models like Vertex AI are able to digest large sets of disparate data, drive analytics and automatically adjust when provided with new information. 

With these machine learning models, retailers can not only incorporate historical sales data, but also use close to real-time data such as marketing campaigns, web actions like a customer clicking the “add to cart” button on a website, local weather forecasts, and much more. 

Pivot point 2: Enhanced product discovery through AI-powered search and recommendations

If customers can’t easily find what they are looking for, whether online or at the store, they will turn to someone else. That’s a simple statement, but one with profound impacts.

In research conducted by The Harris Poll and Google Cloud, we found that over a six month period, 95% of consumers received search results that were not relevant to what they were searching for on a retail website. And roughly 85% of consumers view a brand differently after an unsuccessful search, while 74% say they avoid websites where they’ve experienced search difficulties in the past.

Each year, retailers lose more than $300 billion dollars from search abandonment, or when a consumer searches for a product on a retailer’s website but does not find what they are looking for. Our product discovery solutions help you surface the right products, to the right customers, at the right time. These solutions include: 

• Vision Product Search, which is like bringing the augmented reality of Google Lens to a retailer’s own branded mobile app experience. Both shoppers and retail store associates can search for products using an image they’ve photographed or found online and receive a ranked list of similar items.
• Recommendations AI, which enables retailers to deliver highly personalized recommendations at scale across channels.
• Retail Search, which provides Google-quality search results on a retailer’s own website and mobile applications.

All three are powered by Google Cloud, leveraging Google’s advanced understanding of user context and intent, utilizing technology to deliver a seamless experience to every shopper. With these combined capabilities, retailers are able to reduce search abandonment and improve conversions across their digital properties. 

Pivot point 3: Building the modern store

Stores are no longer places for just browsing and buying. They must be flexible operation centers, ready to pivot to address changing circumstances. The modern store must be multiple things at once: a mini-fulfillment and return center, a recommendation engine, a shopping destination, a fun place to work, and more. 

Just as retail companies had to embrace omnichannel, stores are now becoming omnichannel centers on their own, mixing the digital and physical into a single location. Retailers can use physical stores as a vehicle to deliver superior customer experiences. This will demand heightened levels of collaboration and cooperation between stores, digital, and tech infrastructure teams, building on the agile ways they have worked together.

In many ways, it’s about allowing our physical spaces to function more like digital ones. Google Cloud can help by bringing the scalability, security, and reliability of the cloud to the store, allowing physical locations to upgrade infrastructure and modernize their internal and customer-facing applications. 

Think of it as when a new OS gets released for your phone. It’s the same small, hard box, but the experience can feel radically different. Now, extend that same idea to a digitally enabled store. With the right displays, interfaces, and tools at a given retail location, the team only needs to send an over-the-air update to create radically fresh experiences, ranging from sales displays to fulfillment or employee engagement.

Such an approach can enable streamlined experiences for both customers and store associates. For instance, when it comes to the modern store’s evolving role as a fulfillment or return center, cloud solutions can help drive efficiency in stores through automation of ordering, replenishment, and fulfillment of omnichannel order selection.  

Similar tools for personalized product discovery online can be applied to customers in the store, helping them to browse and explore, or even create a tailored shopping experience. 

The impact of store associates can be maximized by equipping them with technology to provide expertise that drives value-added customer service, as well as increasing productivity in stores by streamlining operations, thus lowering overhead cost. At the register, customers should be able to enjoy frictionless checkout while ensuring reliable, accurate, secure transactions.

Google Cloud can help retailers transform

The ability to adapt and pivot to meet today’s changing consumer needs requires that retailers rely on modern tools to obtain operational flexibility. We believe that every company can be a tech company. That every decision is data driven. That every store is physical and digital all at once. That every worker is a tech worker. 

Google Cloud works with retailers to help them solve their most challenging problems. We have the unique ability to handle massive amounts of unstructured data, in addition to advanced capabilities in AI and ML. Our products and solutions help retailers focus on what’s most important—from improving operations to capturing digital and omnichannel revenue.

In general, the MySQL functionality provided by a Google Cloud’s SQL Database (Cloud SQL) instance is the same as the functionality provided by a locally-hosted MySQL instance.

However, there are a few differences between a standard MySQL instance and a Cloud SQL for MySQL instance.

Unsupported Features

• User defined functions
• InnoDB memcached plugin
• Federated Engine
• The SUPER privilege

Unsupported Statements

Sending any of the following types of SQL statements will generate an error with the message “Error 1290: The MySQL server is running with the google option so it cannot execute this statement”:

• LOAD DATA INFILE
• Note that LOAD DATA LOCALINFILE is supported.
• SELECT … INTO OUTFILE
• SELECT … INTO DUMPFILE
• INSTALL PLUGIN …
• UNINSTALL PLUGIN
• CREATE FUNCTION … SONAME …

Unsupported Statements for Second Generation Instances

The following statements are not supported because Second Generation instances use GTID replication:

• CREATE TABLE … SELECT statements
• CREATE TEMPORARY TABLE statements inside transactions
• Transactions or statements that update both transactional and nontransactional tables

For more information, see the MySQL documentation.

Unsupported Functions

• LOAD_FILE()

Unsupported Client Program Features

• mysqlimport without using the –local option. This is because of the LOAD DATA INFILE restriction. If you need to load data remotely, use the Cloud SQL import function.

• mysqldump using the –tab option or options that are used with –tab. This is because the FILE privilege is not granted for instance users. All other mysqldump options are supported.

• If you want to import databases with binary data into your Cloud SQL for MySQL instance, you must use the –hex-blob option with mysqldump.

• Although hex-blob is not a required flag when you are using a local MySQL server instance and the mysql client, it is required if you want to import any databases with binary data into your Cloud SQL instance. For more information about importing data, see Importing Data.

• Not all MySQL options and parameters are enabled for editing as Cloud SQL flags.

• To request the addition of a configurable Cloud SQL flag, use the Cloud SQL Discussion group.

• For Second Generation instances, InnoDB is the only supported storage engine. For help with converting tables from MyISAM to InnoDB, see the MySQL documentation.

• You cannot import or export triggers, functions, stored procedures, or views into Cloud SQL. However, you can create and use these elements on a Cloud SQL instance.

Notable MySQL Options

Cloud SQL runs MySQL with a specific set of options. If an option might impact how your applications work, we note it here for your information.

skip-name-resolve

This flag impacts how hostnames are resolved for client connections. Learn more.

Cloud SQL for PostgreSQL

Features

• Fully managed PostgreSQL databases in the cloud, based on the Cloud SQL Second Generation platform.
• Custom machine types with up to 416 GB of RAM and 64 CPUs.
• Up to 10TB of storage available, with the ability to automatically increase storage size as needed.
• Create and manage instances in the Google Cloud Platform Console.
• Instances available in US, EU, or Asia.
• Customer data encrypted on Google’s internal networks and in database tables, temporary files, and backups.
• Support for secure external connections with the Cloud SQL Proxy or with the SSL/TLS protocol.
• Data replication between multiple zones with automatic failover.
• Import and export databases using SQL dump files.
• Support for PostgreSQL client-server protocol and standard PostgreSQL connectors.
• Automated and on-demand backups.
• Instance cloning.
• Integration with Stackdriver logging and monitoring.

Some features are not yet available for Cloud SQL for PostgreSQL:

• Point-in-time recovery (PITR)
• Import/export in CSV format using GCP Console or the gcloud command-line tool.

Supported Extensions

Cloud SQL for PostgreSQL supports many PostgreSQL extensions. For a complete list, see PostgreSQL Extensions.

Supported Procedural Languages

Cloud SQL for PostgreSQL supports the PL/pgSQL SQL procedural language.

Supported Languages

You can use Cloud SQL for PostgreSQL with App Engine applications running in the flexible environment that are written in Java, Python, PHP, Node.js, Go, and Ruby. You can also use Cloud SQL for PostgreSQL with external applications using the standard PostgreSQL client-server protocol.

Differences between Cloud SQL and standard PostgreSQL functionality

In general, the PostgreSQL functionality provided by a Cloud SQL instance is the same as the functionality provided by a locally-hosted PostgreSQL instance. However, there are a few differences between a standard PostgreSQL instance and a Cloud SQL for PostgreSQL instance.

Unsupported Features

• Any features that require SUPERUSER privileges. An exception to this rule is made for the CREATE EXTENSION statement, but only for supported extensions.
• Custom background workers

• The psql client in Cloud Shell does not support operations that require a reconnection, such as connecting to a different database using the \c command.

Notable Differences

There are a number of PostgreSQL options and parameters that are not enabled for editing as Cloud SQL flags.

To request the addition of a configurable Cloud SQL flag, use the Cloud SQL Discussion group.